"""
Form input components for the Student Success Predictor
"""
import streamlit as st
from typing import Dict, Any, Tuple
from ..config.mappings import create_feature_mappings, PRESET_CONFIGURATIONS, ACADEMIC_DEFAULTS
def render_preset_selector() -> str:
"""Render the preset selector section"""
st.markdown('', unsafe_allow_html=True)
if preset != "Custom Entry":
st.markdown(
f'โ
Loading {preset} preset values. You can modify any field below.
',
unsafe_allow_html=True)
return preset
def render_personal_info_section(preset: str, feature_maps: Dict) -> Dict[str, Any]:
"""Render personal information input section"""
st.markdown('', unsafe_allow_html=True)
st.markdown("### ๐ค Personal Information")
st.markdown('Basic demographic information about the student
', unsafe_allow_html=True)
defaults = PRESET_CONFIGURATIONS[preset]
marital_status = st.selectbox(
"Marital Status",
options=list(feature_maps['marital_status'].keys()),
format_func=lambda x: feature_maps['marital_status'][x],
index=list(feature_maps['marital_status'].keys()).index(defaults["marital"])
)
gender = st.selectbox(
"Gender",
[0, 1],
format_func=lambda x: "Female" if x == 0 else "Male",
index=defaults["gender"]
)
age_at_enrollment = st.number_input(
"Age at Enrollment",
min_value=17,
max_value=70,
value=defaults["age"],
step=1
)
nationality = st.selectbox(
"Nationality",
options=list(feature_maps['nationality'].keys()),
format_func=lambda x: feature_maps['nationality'][x],
index=0
)
return {
'Marital status': marital_status,
'Gender': gender,
'Age at enrollment': age_at_enrollment,
'Nationality': nationality
}
def render_academic_background_section(preset: str, feature_maps: Dict) -> Dict[str, Any]:
"""Render academic background input section"""
st.markdown('', unsafe_allow_html=True)
st.markdown("### ๐ Academic Background")
st.markdown('Information about the student\'s application and academic history
',
unsafe_allow_html=True)
defaults = PRESET_CONFIGURATIONS[preset]
application_mode = st.selectbox(
"Application Mode",
options=list(feature_maps['application_mode'].keys()),
format_func=lambda x: feature_maps['application_mode'].get(x, f"Mode {x}"),
index=list(feature_maps['application_mode'].keys()).index(defaults["app_mode"]) if defaults["app_mode"] in
feature_maps[
'application_mode'] else 0
)
application_order = st.selectbox(
"Application Order (1st choice, 2nd choice, etc.)",
[1, 2, 3, 4, 5, 6, 7, 8, 9],
index=0
)
course = st.selectbox(
"Course",
options=list(feature_maps['course'].keys()),
format_func=lambda x: feature_maps['course'].get(x, f"Course {x}"),
index=list(feature_maps['course'].keys()).index(defaults["course"]) if defaults["course"] in feature_maps[
'course'] else 0
)
daytime_evening = st.selectbox(
"Attendance",
[0, 1],
format_func=lambda x: "Evening" if x == 0 else "Daytime"
)
previous_qualification = st.selectbox(
"Previous Qualification",
options=list(feature_maps['qualification'].keys()),
format_func=lambda x: feature_maps['qualification'].get(x, f"Qualification {x}")
)
col1, col2 = st.columns(2)
with col1:
previous_qualification_grade = st.number_input(
"Previous Qualification Grade",
min_value=95,
max_value=190,
value=defaults["prev_grade"],
step=1
)
with col2:
admission_grade = st.number_input(
"Admission Grade",
min_value=95,
max_value=190,
value=defaults["adm_grade"],
step=1
)
return {
'Application mode': application_mode,
'Application order': application_order,
'Course': course,
'Daytime/evening attendance': daytime_evening,
'Previous qualification': previous_qualification,
'Previous qualification (grade)': previous_qualification_grade,
'Admission grade': admission_grade
}
def render_family_background_section(preset: str, feature_maps: Dict) -> Dict[str, Any]:
"""Render family background input section"""
st.markdown('', unsafe_allow_html=True)
st.markdown("### ๐จโ๐ฉโ๐งโ๐ฆ Family Background")
st.markdown('Parent education and occupation information
', unsafe_allow_html=True)
defaults = PRESET_CONFIGURATIONS[preset]
mothers_qualification = st.selectbox(
"Mother's Qualification",
options=list(feature_maps['qualification'].keys()),
format_func=lambda x: feature_maps['qualification'].get(x, f"Qualification {x}"),
index=list(feature_maps['qualification'].keys()).index(defaults["mothers_qualification"]) if defaults["mothers_qualification"] in feature_maps['qualification'] else 0
)
fathers_qualification = st.selectbox(
"Father's Qualification",
options=list(feature_maps['qualification'].keys()),
format_func=lambda x: feature_maps['qualification'].get(x, f"Qualification {x}"),
index=list(feature_maps['qualification'].keys()).index(defaults["fathers_qualification"]) if defaults["fathers_qualification"] in feature_maps['qualification'] else 0
)
mothers_occupation = st.selectbox(
"Mother's Occupation",
options=list(feature_maps['occupation'].keys()),
format_func=lambda x: feature_maps['occupation'].get(x, f"Occupation {x}"),
index=list(feature_maps['occupation'].keys()).index(defaults["mothers_occupation"]) if defaults["mothers_occupation"] in feature_maps['occupation'] else 0
)
fathers_occupation = st.selectbox(
"Father's Occupation",
options=list(feature_maps['occupation'].keys()),
format_func=lambda x: feature_maps['occupation'].get(x, f"Occupation {x}"),
index=list(feature_maps['occupation'].keys()).index(defaults["fathers_occupation"]) if defaults["fathers_occupation"] in feature_maps['occupation'] else 0
)
return {
'Mothers qualification': mothers_qualification,
'Fathers qualification': fathers_qualification,
'Mothers occupation': mothers_occupation,
'Fathers occupation': fathers_occupation
}
def render_financial_social_section(preset: str) -> Dict[str, Any]:
"""Render financial and social factors input section"""
st.markdown('', unsafe_allow_html=True)
st.markdown("### ๐ฐ Financial & Social")
st.markdown('Student financial status and special circumstances
',
unsafe_allow_html=True)
defaults = PRESET_CONFIGURATIONS[preset]
col1, col2 = st.columns(2)
with col1:
displaced = st.radio(
"Displaced Student",
[0, 1],
format_func=lambda x: "No" if x == 0 else "Yes",
index=defaults["displaced"],
horizontal=True
)
educational_special_needs = st.radio(
"Educational Special Needs",
[0, 1],
format_func=lambda x: "No" if x == 0 else "Yes",
index=defaults["educational_special_needs"],
horizontal=True
)
debtor = st.radio(
"Has Outstanding Debts",
[0, 1],
format_func=lambda x: "No" if x == 0 else "Yes",
index=defaults["debtor"],
horizontal=True
)
with col2:
tuition_fees_up_to_date = st.radio(
"Tuition Fees Up to Date",
[0, 1],
format_func=lambda x: "No" if x == 0 else "Yes",
index=defaults["tuition_fees_up_to_date"],
horizontal=True
)
scholarship_holder = st.radio(
"Scholarship Holder",
[0, 1],
format_func=lambda x: "No" if x == 0 else "Yes",
index=defaults["scholarship_holder"],
horizontal=True
)
international = st.radio(
"International Student",
[0, 1],
format_func=lambda x: "No" if x == 0 else "Yes",
index=defaults["international"],
horizontal=True
)
return {
'Displaced': displaced,
'Educational special needs': educational_special_needs,
'Debtor': debtor,
'Tuition fees up to date': tuition_fees_up_to_date,
'Scholarship holder': scholarship_holder,
'International': international
}
def render_academic_performance_section(preset: str) -> Dict[str, Any]:
"""Render academic performance input sections"""
academic_defaults = ACADEMIC_DEFAULTS[preset]
# 1st Semester
st.markdown('', unsafe_allow_html=True)
st.markdown("### ๐ 1st Semester Performance")
st.markdown(
'โ ๏ธ Important: Academic performance is a key predictor of student success
',
unsafe_allow_html=True)
col1, col2 = st.columns(2)
with col1:
curricular_units_1st_sem_enrolled = st.number_input(
"Enrolled Units", min_value=0, max_value=26,
value=academic_defaults["1st_sem"]["enrolled"], step=1,
key="1st_enrolled", help="Number of courses registered for"
)
curricular_units_1st_sem_approved = st.number_input(
"Approved Units", min_value=0, max_value=26,
value=academic_defaults["1st_sem"]["approved"], step=1,
key="1st_approved", help="Number of courses passed"
)
curricular_units_1st_sem_credited = st.number_input(
"Credited Units", min_value=0, max_value=26,
value=academic_defaults["1st_sem"]["credited"], step=1,
key="1st_credited", help="Credits from previous education"
)
with col2:
curricular_units_1st_sem_evaluations = st.number_input(
"Evaluations", min_value=0, max_value=45,
value=academic_defaults["1st_sem"]["evaluations"], step=1,
key="1st_evaluations", help="Number of exams taken"
)
curricular_units_1st_sem_grade = st.number_input(
"Average Grade (0-20)", min_value=0, max_value=20,
value=academic_defaults["1st_sem"]["grade"], step=1,
key="1st_grade", help="Average grade on 0-20 scale"
)
curricular_units_1st_sem_without_evaluations = st.number_input(
"Without Evaluations", min_value=0, max_value=14,
value=academic_defaults["1st_sem"]["without"], step=1,
key="1st_without", help="Courses registered but not evaluated"
)
# 2nd Semester
st.markdown('', unsafe_allow_html=True)
st.markdown("### ๐ 2nd Semester Performance")
st.markdown('Second semester data helps improve prediction accuracy
',
unsafe_allow_html=True)
col1, col2 = st.columns(2)
with col1:
curricular_units_2nd_sem_enrolled = st.number_input(
"Enrolled Units", min_value=0, max_value=23,
value=academic_defaults["2nd_sem"]["enrolled"], step=1,
key="2nd_enrolled", help="Number of courses registered for"
)
curricular_units_2nd_sem_approved = st.number_input(
"Approved Units", min_value=0, max_value=20,
value=academic_defaults["2nd_sem"]["approved"], step=1,
key="2nd_approved", help="Number of courses passed"
)
curricular_units_2nd_sem_credited = st.number_input(
"Credited Units", min_value=0, max_value=20,
value=academic_defaults["2nd_sem"]["credited"], step=1,
key="2nd_credited", help="Credits from previous education"
)
with col2:
curricular_units_2nd_sem_evaluations = st.number_input(
"Evaluations", min_value=0, max_value=33,
value=academic_defaults["2nd_sem"]["evaluations"], step=1,
key="2nd_evaluations", help="Number of exams taken"
)
curricular_units_2nd_sem_grade = st.number_input(
"Average Grade (0-20)", min_value=0, max_value=20,
value=academic_defaults["2nd_sem"]["grade"], step=1,
key="2nd_grade", help="Average grade on 0-20 scale"
)
curricular_units_2nd_sem_without_evaluations = st.number_input(
"Without Evaluations", min_value=0, max_value=12,
value=academic_defaults["2nd_sem"]["without"], step=1,
key="2nd_without", help="Courses registered but not evaluated"
)
return {
'Curricular units 1st sem (credited)': curricular_units_1st_sem_credited,
'Curricular units 1st sem (enrolled)': curricular_units_1st_sem_enrolled,
'Curricular units 1st sem (evaluations)': curricular_units_1st_sem_evaluations,
'Curricular units 1st sem (approved)': curricular_units_1st_sem_approved,
'Curricular units 1st sem (grade)': curricular_units_1st_sem_grade,
'Curricular units 1st sem (without evaluations)': curricular_units_1st_sem_without_evaluations,
'Curricular units 2nd sem (credited)': curricular_units_2nd_sem_credited,
'Curricular units 2nd sem (enrolled)': curricular_units_2nd_sem_enrolled,
'Curricular units 2nd sem (evaluations)': curricular_units_2nd_sem_evaluations,
'Curricular units 2nd sem (approved)': curricular_units_2nd_sem_approved,
'Curricular units 2nd sem (grade)': curricular_units_2nd_sem_grade,
'Curricular units 2nd sem (without evaluations)': curricular_units_2nd_sem_without_evaluations
}
def render_economic_indicators_section() -> Dict[str, Any]:
"""Render economic indicators input section"""
st.markdown('', unsafe_allow_html=True)
st.markdown("### ๐ Economic Indicators")
st.markdown(
'๐ Economic context data - typically set by institution based on current conditions
',
unsafe_allow_html=True)
col1, col2, col3 = st.columns(3)
with col1:
unemployment_rate = st.number_input(
"Unemployment Rate (%)",
min_value=7.0, max_value=16.2, value=10.8, step=0.1, format="%.1f"
)
with col2:
inflation_rate = st.number_input(
"Inflation Rate (%)",
min_value=-0.8, max_value=3.7, value=1.4, step=0.1, format="%.1f"
)
with col3:
gdp = st.number_input(
"GDP Growth",
min_value=-4.06, max_value=3.51, value=1.74, step=0.1, format="%.2f"
)
return {
'Unemployment rate': unemployment_rate,
'Inflation rate': inflation_rate,
'GDP': gdp
}